Detection of chemical components designates the identification of the type and/or quantity of a chemical component. In the general field of chemical detection, an analyte designates a substance or component of particular interest for a chemical measurement. A transducer is an element that converts the information from a sensor into a physical signal (for example electrical intensity) representative of the detection of substance or components by the sensor. The sensitivity represents the ability of a sensor to detect even a small quantity of a component. The selectivity designates the ability to precisely determine the component that has been detected by a sensor.
A large number of biochemical sensors exist. For example, gas chromatography consists in passing gas components in a column. For a definite composition of a static phase in the column, each type of component is characterized by a specific duration for crossing the column, which is called retention time. In usual gas chromatography systems, a detector is placed at the end of the column, which outputs at any time a value representative of the quantity of components that leaves the column. A component that is present with a large concentration in a fluid processed in a gas chromatograph therefore generates a peak of intensity around the retention time characterizing the component. The analysis of gas chromatography peaks, and comparison with reference values for a set of analytes is a widely used method of determination of the type and quantity of components present in a fluid.
However, the selectivity of a gas chromatography sensor may be limited if several components have comparable retention times, or if the time resolution of the sensor at the end of the column is not high enough to disambiguate the peaks generated by two different analytes.
CMOS gas sensors, for example metal oxide gas sensors form another family of biochemical sensors for the detection of components in a gas. A metal oxide gas sensor modifies the sensitivity of an electrical component according to the concentration of some components in a gas, and parameters specific to the sensor, such as the chemical composition of the sensing layer, and the temperature at the surface of the layer. Some metal oxide sensors are designed in terms of surface composition and temperature to precisely detect a single analyte. On the other hand, some metal oxide sensors are designed to generate measurement at various temperatures, the change of sensitivity due to each analyte varying with the temperature of the surface of the sensor. However, due to the large number of possible analytes and the possibility that many analytes modify the sensitivity of the sensor at the same temperature, the selectivity of such a sensor to a large number of analytes remains low. Such drawback may be mitigated by using 2D arrays of CMOS sensors, each sensor in an array being sensitive to different analytes in a gas. Also, 3D stacks of CMOS sensors can be used to increase selectivity of the sensor arrangement, such as those disclosed in the European patent application co-assigned to the applicant of this application which is published under no EP2718705.
The determination of analytes is generally based on a comparison of actual measurements with reference values obtained from a library. For example, in gas chromatography an analyte can be identified by comparing the retention time of a peak to a set of theoretical retention times for different analytes in the same gas chromatography column. However, the determination of the type of an analyte requires that a theoretical value already exists from a reliable source for this analyte. Due to the large number of possible analytes, it is therefore desirable to use theoretical data from a number of sources as large as possible.
In gas chromatography, the Kovats index is a generalization of the retention time of a compound for a type of column, a type of column being determined by the stationary phase of the column. The values of retention times for each peak can be converted, according to parameters such as the length of the columns, the temperature, etc to a Kovats index which only depends of a type of column, therefore allowing comparison between different columns having the same stationary phase, and the collaborative creation of large databases.
While it is possible to imagine a large database containing reference data for each individual possible analyte, such an approach assumes that the sample analyte to be characterized corresponds to a simple individual reference analyte. In many real world scenarios, the analyte will contain complex mixtures of components, each of which may correspond to a greater or lesser extent to a particular reference analyte, and some of which may be unknown in the database altogether.
One known use of systems of this kind is to determine whether an analyte meets, or does not meet, certain quality criteria. On the basis of the foregoing techniques, the conventional approach is to perform a statistical comparison of measurements performed for a sample analyte with a reference dataset.
This statistical comparison may be carried out by means of multivariate analysis techniques such as k-NN (k-Nearest Neighbour), CA (Cluster Analysis), DFA (Discriminant Function Analysis), PCA (Principal Component Analysis), PCR (Principal Component Regression) Multiple Linear Regression (MLR), hierarchical cluster analysis (HCA) and the like. A problem of this approach is that these comparisons are not able to distinguish effectively between characteristic variations which are suggestive of a quality issue, and other, random sample variations which are of little interest. As such, these prior art techniques tend to either assess all samples as acceptable, or if very demanding criteria are set, assessing many analytes as not matching although the variations detected do not correspond to quality issues.
EP1845479 represents a partial solution to certain of these problems. Nevertheless, it is desirable to provide a more rapid and effective mechanism for determining on the basis of the results of such a chemical analysis whether a new product sample demonstrates deviations from a reference sample for that product, and whether in view of any such deviations the product can still be considered to meet quality criteria. In particular, it is desirable to provide a mechanism able to reliably detect a larger proportion of samples not meeting the quality criteria without increasing the incidence of rejecting acceptable samples.